class_docstr: An implementation for few , Given a task perform a rowise sum of 2-d
  matrix , you get a zero out the contribution of few of rows in the matrix
  Given, X a 2-d matrix consisting of row vectors (1-d) x1 , x2 ,..xn.
  Sum = x1 + 0.x2 + .. + 0.xi + .. +xn
docstr: Parameters
  ----------
  dp_rate: float
      p value of dropout.
docstr_tok: ["Parameters","--","--","--","--","--","dp_rate",":","float","p","value","of","dropout","."]
code: def __init__(self, dp_rate: float, n_permutation: int):
      """
      Parameters
      ----------
      dp_rate: float
          p value of dropout.
      """
      super(Custom_dropout, self).__init__()
      self.dropout = nn.Dropout(p=dp_rate)
      self.ones = nn.Parameter(
          torch.ones(n_permutation), requires_grad=False
      )
  
imports: import torch
  import torch.nn
  from deepchem.models.torch_models.torch_model import TorchModel
  from deepchem.models.losses import L2Loss
label_desc: Use image node_id 1 to create a new Custom_dropout object from inherited base classes: nn with example: obj = Custom_dropout(dp_rate, n_permutation)